References

• 2015Comparing Predictive Accuracy, Twenty Years Later: A Personal Perspective on the Use and Abuse of Diebold–Mariano Tests494 cited
• 2017A deep learning ensemble approach for crude oil price forecasting383 cited
• 2021Forecasting gold price with the XGBoost algorithm and SHAP interaction values334 cited
• 2018Text-based crude oil price forecasting: A deep learning approach305 cited
• 2020Commodity Price Volatility and the Economic Uncertainty of Pandemics287 cited
• 2016Forecasting the volatility of crude oil futures using HAR-type models with structural breaks269 cited
• 2006A New Method for Crude Oil Price Forecasting Based on Support Vector Machines258 cited
• 2021Crude oil time series prediction model based on LSTM network with chaotic Henry gas solubility optimization256 cited
• 2021A novel ensemble deep learning model for stock prediction based on stock prices and news247 cited
• 2015A novel decomposition ensemble model with extended extreme learning machine for crude oil price forecasting204 cited
• 2006Forecasting Nonlinear Crude Oil Futures Prices176 cited
• 2021Crude oil price prediction: A comparison between AdaBoost-LSTM and AdaBoost-GRU for improving forecasting performance170 cited
• 2016The dynamic linkages between crude oil and natural gas markets155 cited
• 2020Forecasting volatility and co-volatility of crude oil and gold futures: Effects of leverage, jumps, spillovers, and geopolitical risks